Browse Topic: Real-time data
Tillage, a fundamental agricultural practice involving soil preparation for planting, has traditionally relied on mechanical implements with limited real-time data collection or adjustment capabilities. The lack of real-time data and implement statistics results in fleet managers struggling to track performance, driver behavior, and operational efficiency of the implements. Lack of data on vehicle performance can result in unexpected breakdowns and higher maintenance costs, ensuring compliance with regulations is challenging without proper data tracking, potentially leading to fines and legal issues. Bluetooth-enabled mechanical implements for tillage operations represent an emerging frontier in precision agriculture, combining traditional soil preparation techniques with modern wireless technology. Implement mounted battery powered BLE (Bluetooth Low Energy) modules operated by solar panel based rechargeable batteries to power microcontroller. When Implement is operational turns
As mission-critical systems demand more processing power, real-time data movement, and multi-domain interoperability, rugged embedded systems are being transformed. Today's military and aerospace applications increasingly demand the merging of AI computing, enhanced sensor interfaces, and cybersecurity - all under harsh environmental conditions. At the heart of this evolution is the 3U OpenVPX form factor, a modular, compact, and ruggedized hardware standard and increasingly the SOSA aligned subset of the architecture. However, next-generation systems need to go further: supporting higher bandwidth, better thermal efficiency, improved security, while maintaining multi-vendor interoperability and long-term sustainability. We'll discuss some of today's enclosure solutions as well as emerging technologies.
This paper explores the integration of Microsoft Power BI into Model-Based Systems Engineering (MBSE) workflows, specifically within a Model-Based Product Line Engineering (MBPLE) context. Power BI provides a versatile platform for visualizing, analyzing, and manipulating data, enabling users to configure system variants outside traditional MBSE environments while maintaining integration back into the original MBSE model. This approach enhances collaboration between engineering and business disciplines, improves decision-making with real-time data analysis, and allows users to configure and evaluate multiple system variants efficiently. Additionally, the paper discusses how Power BI’s interactive dashboards facilitate better accessibility and analysis, bridging the gap between technical teams and non-technical stakeholders. Future work will focus on improving data pipeline automation and incorporating feature performance metrics to enable real-time trade study analysis, further
This paper aims to explore the application of machine learning techniques to the analysis of road suspension systems, with particular emphasis on mechanical leaf spring suspensions. These systems are essential for vehicle performance, as they guarantee comfort and stability while driving, and they have an intrinsically complex and non-linear dynamic behavior. Because of this complexity, traditional approaches often prove costly and insufficient to represent operating conditions. In this context, machine learning techniques stand out for their ability to learn patterns from experimental data, allowing the modelling of non-linear phenomena that characterize road implement suspensions. One of the main contributions of this study is the demonstration that machine learning algorithms are capable of identifying complex patterns to represent the behavior of the system, as well as facilitating the detection of anomalies and potential faults in the suspension system, contributing to predictive
For mature virtual development, enlarging coverage of performances and driving conditions comparable with physical prototype is important. The subjective evaluation on various driving conditions to find abnormal or nonlinear phenomena as well as objective evaluation becomes indispensable even in virtual development stage. From the previous research, the road noise had been successfully predicted and replayed from the synthesis of system models. In this study, model based NVH simulator dedicated to virtual development have been implemented. At first, in addition to road noise, motor noise was predicted from experimental models such as blocked force and transfer function of motor, mount and body according to various vehicle conditions such as speed and torque. Next, to convert driver’s inputs such as acceleration and brake pedal, mode selection button and steering wheel to vehicle’s driving conditions, 1-D performance model was generated and calibrated. Finally, the audio and visual
The deployment of PEM fuel cell systems is becoming an increasingly pivotal aspect of the electrification of the transport sector, particularly in the context of heavy-duty vehicles. One of the principal constraints to market penetration is durability of the fuel cell which hardly meets the expected targets set by the vehicle manufacturers and regulatory bodies. Over the years, researchers and companies have faced the challenge of developing reliable diagnostic and condition monitoring tools to prevent early degradation and efficiency losses of fuel cell stack. The diagnostic tools for fuel cell rely usually on model-based, data driven and hybrid approaches. Most of these are mainly developed for stationary and offline applications, with a lack of suitable methods for real-time and vehicle applications. The work presented is divided into two parts: the first part explores the main degradation conditions for a PEMFC and characteristics, advantages, and application limits of the main
U.S. Army Combat Capabilities Development Command Chemical Biological Center (DEVCOM CBC) researchers are developing a way to scan for chemical biological agent on surfaces on the fly. Literally on the fly as it consists of an AI-enabled spectrometer mounted on an unmanned aerial vehicle (UAV) or unmanned ground vehicle (UGV) sending back vital data in real time. It is called Hyperspectral Threat Anomaly Detection, or HyperThreAD for short.
To meet the requirements of high-precision and stable positioning for autonomous driving vehicles in complex urban environments, this paper designs and develops a multi-sensor fusion intelligent driving hardware and software system based on BDS, IMU, and LiDAR. This system aims to fill the current gap in hardware platform construction and practical verification within multi-sensor fusion technology. Although multi-sensor fusion positioning algorithms have made significant progress in recent years, their application and validation on real hardware platforms remain limited. To address this issue, the system integrates BDS dual antennas, IMU, and LiDAR sensors, enhancing signal reception stability through an optimized layout design and improving hardware structure to accommodate real-time data acquisition and processing in complex environments. The system’s software design is based on factor graph optimization algorithms, which use the global positioning data provided by BDS to constrain
Intelligent Structural Health Monitoring (SHM) of bridge is a technology that utilizes advanced sensor technology along with professional bridge engineering knowledge, coupled with machine vision and other intelligent methods for continuously monitoring and evaluating the status of bridge structures. One application of SHM technology for bridges by way of machine learning is in the use of damage detection and quantification. In this way, changes in bridge conditions can be analyzed efficiently and accurately, ensuring stable operational performance throughout the lifecycle of the bridge. However, in the field of damage detection, although machine vision can effectively identify and quantify existing damages, it still lacks accuracy for predicting future damage trends based on real-time data. Such shortfall l may lead to late addressing of potential safety hazards, causing accelerated damage development and threatening structural safety. To tackle this problem, this study designs a deep
This SAE Aerospace Recommended Practice (ARP) provides an algorithm aimed to analyze flight control surface actuator movements with the objective to generate duty cycle data applicable to hydraulic actuator dynamic seals.
Researchers have developed a new method for predicting what data wireless computing users will need before they need it, making wireless networks faster and more reliable. The new method makes use of a technique called a “digital twin,” which effectively clones the network it is supporting.
Tracking of energy consumption has become more difficult as demand and value for energy have increased. In such a case, energy consumption should be monitored regularly, and the power consumption want to be reduced to ensure that the needy receive power promptly. Our objective is to identify the energy consumption of an electric vehicle from battery and track the daily usage of it. We have to send the data to both the user and provider. We have to optimize the power usage by using anomaly detection technique by implementing deep learning algorithms. Here we are going to employ a LSTM auto-encoder algorithm to detect anomalies in this case. Estimating the power requirements of diverse locations and detecting harmful actions are critical in a smart grid. The work of identifying aberrant power consumption data is vital and it is hard to assure the smart meter’s efficiency. The LSTM auto-encoder neural network technique is used here for predicting power consumption and to detect anomalies
A tactile perception system provides human-like multimodal tactile information to objects like robots and wearable devices that require tactile data in real time. The research team developed a real-time and multi-modal tactile detection system by mimicking the principle by which various types of tactile information is perceived by a variety of sensory receptors in the human skin and is transmitted to the brain in real time.
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